Machine Learning Engineer at Red Hat focused on AI model optimization and collaboration with research teams. Developing software for LLM training and deployment in enterprise settings.
About the role
- Machine Learning Engineer developing AI-driven fitness solutions at BHOUT. Analyzing performance data and building predictive models to coach users effectively.
Responsibilities
- Analyze and process tabular performance data from existing pipelines.
- Design, train, and evaluate machine learning models for prediction, clustering, and recommendation tasks.
- Define and track relevant metrics, identifying trends and patterns over time.
- Develop data-driven systems to forecast user performance and progression.
- Build personalized recommendation systems based on user metrics.
- Perform data cleaning, preprocessing, and feature engineering.
- Collaborate with Computer Vision and app teams to integrate outputs into the broader product.
- Ensure models are efficient and suitable for low-latency environments when required.
Requirements
- 2+ years of experience in Machine Learning and Data Analysis.
- Strong experience working with tabular data.
- Proficiency in SQL and experience with database management.
- Experience with data cleaning, preprocessing, and visualization.
- Solid understanding of core ML algorithms such as PCA, t-SNE, K-means, Decision Trees, Bagging & Boosting (XGBoost), least squares, ARIMA, among others.
- Proficiency in Python, including libraries such as Pandas, NumPy, and Scikit-learn.
- Experience training, evaluating, and deploying ML models.
- Good understanding of model evaluation metrics and validation techniques.
- Awareness of resource constraints (memory, compute, latency).
- Strong academic background (Master’s or PhD in a relevant field preferred).
- Ability to work in multidisciplinary teams.
- Nice to have: Experience with NLP, conversational AI, or language models.
- Experience with multimodal AI (e.g., vision-language models, CLIP).
- Familiarity with speech synthesis or voice-based interaction systems.
- Understanding of edge AI frameworks such as TensorRT, TFLite, or ONNX Runtime Mobile.
- Experience with MLOps tools such as MLflow or Weights & Biases.
- Experience deploying chat models or AI assistants, ideally in fitness, gaming, or sports coaching contexts.
- Familiarity with reinforcement learning from human feedback (RLHF).
Benefits
- Play BHOUT: You and your family members can enjoy our special BHOUT Club Memberships.
- Competitive Compensation: Competitive salary and performance-based Bonuses.
- Extra Time Off: Enjoy 25 vacation days per year, plus 3 additional days off on your birthday, Christmas Eve, and New Year’s Eve.
- Health Insurance: Access to a wide network of clinics and hospitals, negotiated rates, and reimbursement of medical expenses. For you and your immediate family, in Portugal only.
- Work-Life Balance: Work on your own terms with BHOUT's Flexible Hours and Remote Work Options!
- Entrepreneurial Environment: Dynamic atmosphere where every voice is heard, and every idea has the potential to make a significant impact.
- Team Get-Togethers: Regular Team Events and Off-sites to foster Collaboration and Camaraderie!
Job title
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